This invention relates generally to revenue causality analyzer system and method to provide fast and efficient analysis of revenue causality for price management and business management. More particularly, the present invention relates to a method for analyzing changes in revenue over two time periods to attribute the components causally responsible for the change in revenue.
Between two time periods revenues change. The difference in revenues between the time periods is the change in revenue. An understanding of change in revenue causality is very important to effective price management. As such, there is a desire to accurately be able to attribute changes in revenue to causal factors. Within this application causal factors may also be referred to as causal effects or causalities. These factors include changes in product pricing, changes in the volume of products sold, changes in product mix sold, changes in costs, changes in exchange rates, and any additional factors that may play a role in revenue changes.
When revenue change is due to only one causality factor, the causality analysis is very easily determined. However, when multiple factors are involved it traditionally has been difficult, if not impossible, to attribute the amount of change of revenue to the appropriate factors. Additionally, with the more products included in the system the more complex the analysis becomes.
Currently, human intuition must be utilized to make a rough estimation of the importance of each causal factor to the change in revenue. Alternatively, computer systems may be utilized that provide some measure of revenue causality attribution, however, these current systems either make gross estimations in their computation, or are intermittently able to analyze revenue causality. Additionally, these computations may be difficult and may require large processing resources to effectuate, especially when the system includes large numbers of products
For the typical business, the above systems are still too inaccurate, unreliable, and intractable in order to be utilized effectively for price management and analysis. Businesses, particularly those involving large product sets, would benefit greatly from the ability to have accurate revenue causality characterization.
It is therefore apparent that an urgent need exists for an improved system and method for revenue causality analysis and attribution that is both accurate and efficient. This solution would replace current revenue analysis techniques with a more accurate system; thereby increasing effectiveness in downstream price management that utilizes the revenue analysis.